Wireless networks are utilized for real-time, closed-loop sensing and control in networked cyber-physical systems. For instance, wireless networking standards have been defined for industrial monitoring and control, wireless networks have been deployed for industrial automation, and the automotive industry has also been exploring the application of wireless networks to inter-vehicle as well as intra-vehicle sensing and control.
In wireless networked sensing and control (WSC) systems, message passing across wireless networks (or wireless messaging for short) allows for coordination among distributed sensors, controllers, and actuators. When supporting mission-critical tasks such as industrial process controls, wireless messaging is required to be reliable (i.e., having high delivery ratio) and to be in real-time. Current wireless messaging systems are subject to inherent dynamics and uncertainties. Co-channel interference is a major source of uncertainty due to collisions of concurrent transmissions. Thus, scheduling transmissions to prevent co-channel interference is a basic element of wireless messaging in WSC systems.
In WSC systems, not only do wireless link dynamics introduce uncertainty as in traditional wireless sensing/control networks, dynamic control strategies also introduce dynamic network traffic patterns and pose different requirements on messaging reliability and timeliness. For agile adaptation to uncertainties and for avoiding information inconsistency in centralized scheduling, distributed scheduling becomes desirable for interference control in WSC networks. Most existing systems are either based on a physical interference model or a protocol interference model.
In the physical model, a set of concurrent transmissions (Si, Ri), i=1 . . . N, are regarded as not interfering with one another if the following conditions hold true: ((PSi, Ri)/(Ni+Σj=1 . . . N,j≠iPSj,Ri))≥γ, i=1 . . . N, where PSi,Ri and PSj,Ri is the strength of signals reaching the receiver Ri from the transmitter Si and Sj respectively, Ni is the background noise power at receiver Ri, and γ is the signal-to-interference-plus-noise-ratio (SINR) threshold required to ensure a certain link reliability. In the protocol model, a transmission from a sending node S to a corresponding receiver node R is regarded as not being interfered by a concurrent transmitter C if DC,R≥K×DS,R, where DC,R is the geographic distance between C and R, DS,R is the geographic distance between S and R, and K is a constant number.
The physical model is a high-fidelity interference model in general, but interference relations defined by the physical model are non-local and are combinatorial because whether one transmission interferes with another depends on the other transmissions in the network. Even though many centralized TDMA scheduling algorithms have been proposed based on the physical model, distributed physical-model-based scheduling still has drawbacks. It converges slowly due to explicit network-wide coordination, it has to employ strong assumptions such as the knowledge of node locations, it ignores cumulative interference which introduces uncertainties in communication, and it is not suitable for dynamic network settings due to a need for centrally computing an interference set of each link (i.e., the set of links interfering with the link) or the interference neighborhood of each link (i.e., the set of links causing non-negligible interference to the link). The challenge of designing scheduling protocols when interfering links are beyond the communication range of one another is similarly not addressed in existing physical-model-based scheduling algorithms.
Unlike the physical model, the protocol model defines local, pairwise interference relations. The locality of the protocol model enables agile protocol adaptation in the presence of uncertainties. However, the protocol model is usually inaccurate, thus scheduling based on the protocol model or variants of the protocol model does not ensure link reliability and also tends to reduce network throughput.
Besides scheduling based on the physical and protocol interference models, distributed scheduling algorithms using general pairwise interference models have also been proposed. The distributed scheduling algorithms do not address how to identify the interference set of each link, and their implementations assume a model similar to the protocol model. These algorithms do not address important systems issues such as how to design scheduling protocols when interfering links are beyond the communication range of one another.
To bridge the gap between the existing interference models and the design of distributed, field-deployable scheduling protocols with predictable data delivery reliability and timeliness, a major challenge is to develop an interference model that is both local and of high-fidelity.